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Hosein K. Nazari

Publications and source records attributed to Hosein K. Nazari.

3 recordsLinked to original sources

Fly-PRAC: Packet Recovery for Random Linear Network Coding

Network Coding (NC) is a compelling solution for increasing network efficiency. However, it discards corrupted packets and cannot achieve optimal performance in noisy communications. Since most of the information in corrupted packets is error-free, discarding them is not the best strategy. Several packet recovery techniques such as PRAC and S-PRAC were proposed to exploit corrupted packets. Yet, they are slow and only practical when the packet size is small and communication channels are not very noisy. We propose a packet recovery scheme called Fly-PRAC to address these issues. Fly-PRAC exploits algebraic relations between a group of coded packets to estimate their corrupted parts and recovers them. Unlike previous schemes, Fly-PRAC can recover coded packets at the intermediate node without decoding them. We have compared Fly-PRAC against S-PRAC. Results show when the bit error rate (ε) is 10^-4, Fly-PRAC outperforms S-PRAC by two folds for a payload of 900B. In two-hop communication with ε = 10^-4 and a payload size of 500B, by enabling the recovery in the intermediate node, Fly-PRAC reduces transmissions by 16%. In a Sparse Network Coding (SNC) scenario, with two non-zero elements in the coefficient vectors and a payload of 800B, there is a reduction by 31% on average for decoding delay.

cs.IT↗

An IoT Framework for Building Energy Optimization Using Machine Learning-based MPC

This study proposes a machine learning-based Model Predictive Control (MPC) approach for controlling Air Handling Unit (AHU) systems by employing an Internet of Things (IoT) framework. The proposed framework utilizes an Artificial Neural Network (ANN) to provide dynamic-linear thermal model parameters considering building information and disturbances in real time, thereby facilitating the practical MPC of the AHU system. The proposed framework allows users to establish new setpoints for a closed-loop control system, enabling customization of the thermal environment to meet individual needs with minimal use of the AHU. The experimental results demonstrate the cost benefits of the proposed machine-learning-based MPC-IoT framework, achieving a 57.59\% reduction in electricity consumption compared with a clock-based manual controller while maintaining a high level of user satisfaction. The proposed framework offers remarkable flexibility and effectiveness, even in legacy systems with limited building information, making it a pragmatic and valuable solution for enhancing the energy efficiency and user comfort in pre-existing structures.

eess.SY↗

TSN-FlexTest: Flexible TSN Measurement Testbed (Extended Version)

Robust, reliable, and deterministic networks are essential for a variety of applications. In order to provide guaranteed communication network services, Time-Sensitive Networking (TSN) unites a set of standards for time-synchronization, flow control, enhanced reliability, and management. We design the TSN-FlexTest testbed with generic commodity hardware and open-source software components to enable flexible TSN measurements. We have conducted extensive measurements to validate the TSN-FlexTest testbed and to examine TSN characteristics. The measurements provide insights into the effects of TSN configurations, such as increasing the number of synchronization messages for the Precision Time Protocol, indicating that a measurement accuracy of 15 ns can be achieved. The TSN measurements included extensive evaluations of the Time-aware Shaper (TAS) for sets of Tactile Internet (TI) packet traffic streams. The measurements elucidate the effects of different scheduling and shaping approaches, while revealing the need for pervasive network control that synchronizes the sending nodes with the network switches. We present the first measurements of distributed TAS with synchronized senders on a commodity hardware testbed, demonstrating the same Quality-of-Service as with dedicated wires for high-priority TI streams despite a 200% over-saturation cross traffic load. The testbed is provided as an open-source project to facilitate future TSN research.

cs.NI↗